EFRI BRAID: Brain-inspired Algorithms for Autonomous Robots (BAAR)

EFRI BRAID：自主机器人的类脑算法 (BAAR)

• 批准号: 2318065
• 负责人: Junmin Wang
• 金额: $ 200万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318065&HistoricalAwards=false
• 关键词: EFRI; BRAID; Brain; inspired; Algorithms

Autonomous robots, such as self-driving vehicles (SDVs) and household collaborative robots (Cobots), possess great potential to benefit society and meet several important national needs. Although artificial intelligence (AI) has made substantial progress, the current data/computational efficiency and adaptability of autonomous robots pale in comparison to humans in performing routine sensorimotor tasks such as driving and cooking. Enabling such autonomous robots to continually learn from experience and persistently improve their efficiency and resilience in the real world as humans do is imperative for their widespread deployments. This project aims to develop novel computational algorithms for robot autonomy with principles and insights of neurobiological learning and brain intelligence. The outcomes could make a multifaceted and transformative impact on autonomous robots such as SDVs, Cobots, and other intelligent robotic systems in manufacturing and healthcare applications that face the same challenges of computational/data inefficiency and adaptation inflexibility.The project seeks to provide a paradigm shift in autonomous robotic systems by incorporating brain-inspired intelligence throughout their fundamental and core capabilities of perception, planning, and continual learning. Using convergent engineering-science approaches, the project aims to create a fundamental and innovative framework of brain-inspired perception, learning, and planning algorithms for autonomous robots. The framework will be applied to SDVs and Cobots as two representative and complementary engineering systems through combined theoretical and empirical studies. Integrating brain-inspired innovations, the work will adapt and engineer the general brain-inspired methods and algorithms to SDVs and Cobots for experimental validation of the effectiveness in data- and energy-efficiency, adaptability, and resiliency. It is expected that the findings will not only provide a significant leap to SDVs and Cobots toward their real-world deployments, but also have a transformative impact on other intelligent robotic systems such as those in manufacturing and healthcare domains by improving their data/computation efficiency, adaptation resiliency, and intelligence interpretability.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

自主机器人，如自动驾驶汽车（SDV）和家庭协作机器人（Cobots），具有造福社会和满足几个重要国家需求的巨大潜力。 尽管人工智能（AI）已经取得了实质性的进展，但与人类相比，自主机器人当前的数据/计算效率和适应性在执行驾驶和烹饪等常规感觉运动任务方面相形见绌。 使这种自主机器人能够像人类一样不断从经验中学习，并在真实的世界中持续提高其效率和弹性，这对它们的广泛部署至关重要。该项目旨在利用神经生物学学习和大脑智能的原理和见解，开发用于机器人自主性的新型计算算法。 这些成果可能会对自主机器人产生多方面的变革性影响，例如SDV，Cobots以及制造和医疗保健应用中面临计算/数据效率低下和适应能力低下的其他智能机器人系统。该项目旨在通过将大脑启发的智能融入自主机器人系统的感知，规划，和不断学习。该项目使用融合的工程科学方法，旨在为自主机器人创建一个基础和创新的大脑感知，学习和规划算法框架。该框架将应用于SDV和Cobots作为两个代表性和互补的工程系统，通过结合理论和实证研究。这项工作将整合受大脑启发的创新，将一般受大脑启发的方法和算法调整和设计到SDV和Cobots，以实验验证数据和能源效率，适应性和弹性的有效性。 预计这些发现不仅将为SDV和Cobots提供面向现实世界部署的重大飞跃，而且还将对其他智能机器人系统产生变革性影响，例如通过提高其数据/计算效率，适应弹性，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值进行评估来支持和更广泛的影响审查标准。